class_docstr: Random augment with fixed magnitude.
class_docstr_tok: ["Random","augment","with","fixed","magnitude","."]
docstr: Create a RandAugment instance.
  
  Args:
    n: number of augmentations to perform per image.
    p: probability to apply an augmentation. If None then always apply.
    magnitude: default magnitude in range [0, 1]; magnitude will be chosen randomly when None.
    quantum: number of levels of quantization for the magnitude.
docstr_tok: ["Create","a","RandAugment","instance",".","Args",":","n",":","number","of","augmentations","to","perform","per","image",".","p",":","probability","to","apply","an","augmentation",".","If","None","then","always","apply",".","magnitude",":","default","magnitude","in","range","[","0",",","1","]",";","magnitude","will","be","chosen","randomly","when","None",".","quantum",":","number","of","levels","of","quantization","for","the","magnitude","."]
code: def __init__(
      self,
      n: int = 2,
      p: Optional[float] = None,
      magnitude: Optional[float] = None,
      quantum: int = 10,
  ):
      """Create a RandAugment instance.
  
      Args:
        n: number of augmentations to perform per image.
        p: probability to apply an augmentation. If None then always apply.
        magnitude: default magnitude in range [0, 1]; magnitude will be chosen randomly when None.
        quantum: number of levels of quantization for the magnitude.
      """
      self.n = n
      self.p = p
      self.magnitude = magnitude
      self.quantum = quantum
  
file_path: objax/examples/fixmatch/libml/augment/randaugment/randaugment.py
imports: from typing import Optional
  import tensorflow
  from examples.fixmatch.libml.augment.randaugment import augment_ops
label_desc: Use image node_id 1 to create a new RandAugment object  with example: obj = RandAugment(n, p, magnitude, quantum)